Free floating electric car sharing design: Data driven optimisation

For accurate object vehicle estimation using radar, there are two fundamental problems: measurement uncertainties in calculating an object’s position with a virtual polygon box and latency due to commercial radar tracking algorithms. We present a data-driven object vehicle estimation scheme to solve measurement uncertainty and latency problems in radar systems. A radar accuracy model and latency coordination are proposed to reduce the tracking error. We first design data-driven radar accuracy models to improve the accuracy of estimation determined by the object vehicle’s position. The proposed model solves the measurement uncertainty problem within a feasible set for error covariance. The latency coordination is developed by analyzing the position error according to the relative velocity. The position error by latency is stored in a feasible set for relative velocity, and the solution is calculated from the given relative velocity. Removing the measurement uncertainty and latency of the radar system allows for a weighted interpolation to be applied to estimate the position of the object vehicle. Our method is tested by a scenario-based estimation experiment to validate the usefulness of the proposed data-driven object vehicle estimation scheme. We confirm that the proposed estimation method produces improved performance over the conventional radar estimation and previous methods.

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Impact of Charging Infrastructure and Policies on Electric Car Sharing Systems

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) ◽

10.1109/itsc45102.2020.9294282 ◽

2020 ◽

Author(s):

Alessandro Ciociola ◽

Dena Markudova ◽

Luca Vassio ◽

Danilo Giordano ◽

Marco Mellia ◽

...

Keyword(s):

Car Sharing ◽

Charging Infrastructure ◽

Electric Car

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Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization

Geosciences ◽

10.3390/geosciences11020099 ◽

2021 ◽

Vol 11 (2) ◽

pp. 99 ◽

Cited By ~ 1

Author(s):

Yueqi Gu ◽

Orhun Aydin ◽

Jacqueline Sosa

Keyword(s):

Los Angeles ◽

A Priori ◽

Safety Data ◽

Spatial Optimization ◽

Optimal Number ◽

Multidisciplinary Optimization ◽

Data Driven ◽

Fault System ◽

Design Data ◽

The Impact

Post-earthquake relief zone planning is a multidisciplinary optimization problem, which required delineating zones that seek to minimize the loss of life and property. In this study, we offer an end-to-end workflow to define relief zone suitability and equitable relief service zones for Los Angeles (LA) County. In particular, we address the impact of a tsunami in the study due to LA’s high spatial complexities in terms of clustering of population along the coastline, and a complicated inland fault system. We design data-driven earthquake relief zones with a wide variety of inputs, including geological features, population, and public safety. Data-driven zones were generated by solving the p-median problem with the Teitz–Bart algorithm without any a priori knowledge of optimal relief zones. We define the metrics to determine the optimal number of relief zones as a part of the proposed workflow. Finally, we measure the impacts of a tsunami in LA County by comparing data-driven relief zone maps for a case with a tsunami and a case without a tsunami. Our results show that the impact of the tsunami on the relief zones can extend up to 160 km inland from the study area.

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Model to Optimize Location and Planning of Electric Car Sharing Rental Site

CICTP 2017 ◽

10.1061/9780784480915.408 ◽

2018 ◽

Author(s):

Ye Li ◽

Jing Fan

Keyword(s):

Car Sharing ◽

Electric Car

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Modelling and Performance Analysis of Electric Car-Sharing Systems Using Petri Nets

E3S Web of Conferences ◽

10.1051/e3sconf/202017003001 ◽

2020 ◽

Vol 170 ◽

pp. 03001 ◽

Cited By ~ 1

Author(s):

A. Hamroun ◽

K. Labadi ◽

M. Lazri

Keyword(s):

Petri Nets ◽

Simulation Analysis ◽

Discrete Event ◽

Stochastic Petri Nets ◽

Car Sharing ◽

Event Simulation ◽

Electric Car ◽

Bike Sharing ◽

And Performance ◽

Charging Stations

Car sharing systems emerged as a new answer to mobility challenges in smart and sustainable cities. Despite their apparent success, design and exploitation of such systems raise crucial strategic and operational challenges. To help planners and decision makers, simulation, analysis and optimization models are unavoidable. Based on the formal modelling and analysis power of stochastic Petri nets, this paper proposes a discrete event simulation model for electric car sharing systems for performance and analysis purposes, taking into account their complex dynamic behaviour, organization and parameters including capacities of the stations, battery and energy availability, locations of charging stations and also their car maintenance activities, not negligible compared to the case of bike-sharing systems.

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